Agriculture has remained the foundation for survival of human civilization for centuries and before. It has gone through a number of phases of transformations over the human existence timeline. Traditional farming activities like Land preparation, Sowing, Irrigation, Fertilization, Weeding, Pest and Disease control, Harvesting and Post-harvest activities are done manually with minimal automation tools available. Today also farmers face challenges like low productivity, climate change, pests and disease, and scarcity of resources. Artificial Intelligence (AI) has emerged as a transformative force in modern agriculture, offering innovative solutions to address the growing challenges faced by the farming fraternity. This paper explores various applications of AI in the agriculture sector, considering its potential to improve productivity, sustainability, and efficiency of agriculture practices. The key areas of AI implementation in farming include precision farming, crop prediction, pest and disease detection and control, automated irrigation, seeding and harvesting systems, and soil health monitoring. Advanced machine learning techniques and data analytics enables farmers to make informed - data-driven decisions, optimize resource usage, and reduce environmental impacts. Additionally, AI-driven robots and drones are revolutionizing tasks such as planting, sowing, watering, harvesting, pest management and crop monitoring, leading to labour cost savings and improved yields. Despite of providing numerous benefits, the adoption of AI in the agriculture sector faces a number of challenges, including easy access to technology, data privacy issues, technical infrastructure and the need for skilled labour. The paper discusses challenges, future trends, opportunities, and the potential for AI to shape the future of sustainable agricultural practices at the end. As the scope of AI for the agriculture sector continues to evolve, this paper will help researchers to explore applications of AI in the agriculture sector in depth and develop context-specific AI solutions to address existing challenges in the farming sector. The paper will also help in exploring socio-economic impacts of adapting AI, and considering farmer’s education and training for easy adaptation of AI